Abstract
The TELAVIV system for computerised arrhythmia detection and identification in ambulatory ECGs has been extended to include a reliable and rapid computerised method for ST-segment analysis. It now incorporates self-learning analysis and classification of ST-segment changes. Some advantages for ST-segment analysis present in this algorithm are: (a) it analyses three channels simultaneously instead of one or two in other systems; (b) it includes a larger number of parameters describing the ST-segment changes; (c) the measurements are detected from a noise-free template, averaged over 20 beats; (d) isoelectric and J-points are detected by a self-learning process; (e) a scoring system has been added; and (f) it now has the capability to detect and classify arrhythmias and ST-segment changes simultaneously. The system was tested on three types of recordings: 30 1 h recordings in which ST-changes were observed in the quick summary plot produced by the TELAVIV system; 12 recordings taken during submaximal effort; and 13 recordings made during conventional ergometry. The results indicated a high level of sensitivity and specificity of the program.
Original language | English |
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Pages (from-to) | 513-519 |
Number of pages | 7 |
Journal | Medical and Biological Engineering and Computing |
Volume | 25 |
Issue number | 5 |
DOIs | |
State | Published - Sep 1987 |
Externally published | Yes |
Keywords
- Ambulatory monitoring
- Computerised detection
- ST segment
ASJC Scopus subject areas
- Biomedical Engineering
- Computer Science Applications